https://github.com/arunr1408/dysarthric-speech-recognition-matlab

A project to classify dysarthric and non-dysarthric speech using deep learning techniques.
https://github.com/arunr1408/dysarthric-speech-recognition-matlab

cnn deep-learning dysarthria matlab speech-processing speech-recognition

Last synced: 24 days ago
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A project to classify dysarthric and non-dysarthric speech using deep learning techniques.

• Host: GitHub
• URL: https://github.com/arunr1408/dysarthric-speech-recognition-matlab
• Owner: ArunR1408
• License: apache-2.0
• Created: 2024-07-30T18:03:19.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-07-30T18:05:28.000Z (5 months ago)
• Last Synced: 2024-10-25T04:23:42.305Z (2 months ago)
• Topics: cnn, deep-learning, dysarthria, matlab, speech-processing, speech-recognition
• Language: MATLAB
• Homepage:
• Size: 24.4 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Dysarthric Speech Recognition

## Project Overview

This project aims to classify dysarthric and non-dysarthric speech using deep learning techniques. The dataset consists of audio samples from dysarthric and non-dysarthric individuals, with the goal of developing a model that can accurately distinguish between the two classes.

## Features

- Speech waveplot visualization

- Spectrogram analysis

- Zero-crossing rate (ZCR) calculation and visualization

- Feature extraction using MFCC

- Deep learning model implementation using CNN

## Data Information

The dataset contains 2000 audio samples divided into four categories:

- Dysarthric females: 500 samples

- Dysarthric males: 500 samples

- Non-dysarthric females: 500 samples

- Non-dysarthric males: 500 samples

### Data CSV Information

The `data.csv` file contains the following columns:

- `filename`: Path to the audio file

- `is_dysarthria`: Indicates if the sample is from a dysarthric individual (dysarthria or non_dysarthria)

- `gender`: Gender of the speaker (male or female)

## Usage

1. Clone the repository.

2. Ensure the dataset is located in the specified directory.

3. Run `main.m` to load data, visualize waveplots, spectrograms, and ZCRs, and train the CNN model.

## Work in Progress

This project is a work in progress, and additional features and improvements are being actively developed.

## License

This project is licensed under the MIT License.